Efficiently and effectively analyzing the vast amount of data currently available for automated analysis, such as is available from many diverse automated information systems, is often difficult. The field of Information Analytics, or Data Mining, is developing techniques to more efficiently handle the vast amounts of currently available data and also the ever increasing amounts of data being collected daily. Effective analysis of existing data is applicable to, for example, retailers to help optimize supply chains and to help predict the needs of customers. Various data collection and analysis entities process very large volumes of email, conversations, and other types of data. Healthcare researchers process large silos of health data to discover medication and adverse event correlations. Environmental scientists can leverage data mining algorithms to significantly reduce the time it takes to verify hypotheses.
However, the various problems that can be solved through data analyses are not all amenable to the same type of data analysis. Answers to some problems are better obtained by numeric analysis, and some are better obtained through semantic analysis of unstructured data.
Therefore, the efficiently of obtaining results to a posed question is limited by a lack of uniformity of analyses to be applied.